The proliferation of digital transformation: from ‘nice to have’ to a necessity for success

The overarching concept of digital transformation is to use technology to replace manual processes with automated, digital ones and to replace older, legacy systems with modern, agile technologies.

Digital transformation is now top of mind for many companies

What was historically seen as “a good idea for the future” or a nice add-on or update to an outdated system has now become mission critical.

Digital transformation has become the benchmark for survival in the financial market, making it even more critical for success and innovation. As next-gen technology continues to transform, financial services companies and their executive teams are bracing for the next phase of digital transformation.

And most recognise the need to embrace this acceleration towards digital but are still too early in their journey to undergo technology innovation, tech productivity and platform modernisation, which will not only set them up for success in the marketplace, but also set them apart from competitors.

While it may sound simple, the challenge these organisations face is significant. Obvious resource roadblocks exist such as time, money and people, while digitisation has also swept into the regulatory landscape, rapidly becoming the only way to meet strict regulatory guidelines across global markets.

Faced with new or changing regulations continuously, financial services firms must radically change the way they handle these new requests. Plus, even looking past the increasingly demanding regulatory environment, the overwhelming amount of data available is becoming more and more time consuming and costly to collect, sort and analyse – and less and less manageable for humans alone.

At a tipping point, these firms must rely on next-gen technology that offers more effective data management. Fortunately, as regulations and data have evolved, so have the capabilities of today’s technology, with the advent of AI, blockchain and digital assets that are automating processes and workflows.

Terminology and technology: What does it all mean?

We all hear the tech lingo so much, sometimes it feels like it’s lost all real meaning. However, understanding the nuances of terms like hyper-automation, artificial intelligence (AI), machine learning (ML), intelligent document processing (IDP) and natural language processing (NLP) makes a significant difference for the decision makers, who are ready to embrace innovation and the benefits that follow.

While AI is a computer system’s ability to perform tasks that typically require human context and intelligence, ML and NLP are more like subsets of AI. They can be divided by different types of technology – some of which require ML to function while others require NLP. On the other hand, they can be divided by level of intelligence within an AI machine.

An automatic, predictive approach to AI, ML allows the machine to learn (for lack of a better word) from historic data. This can be applied in a variety of different ways – think of how chatbots use speech recognition or how you unlock your phone with facial recognition – but it can also be used for more accurate medical diagnoses or to extract data from a document.

Further, NLP applies ML to human language, interpreting it for the technology to use. By using NLP and AI, new technologies can be even more precise, extracting key terms and details from loan documents while also reducing time and increasing accuracy over manually processing the information – a great way to combat those aforementioned regulatory and compliance standards.

Most people will agree, the complexity of this often seems like a great reason to avoid digital transformation all together. Fear is often the leading barrier in organisations moving forward with digital transformation because let’s face it, no one wants to undergo a project they don’t understand.

Fortunately, adopting a new technology doesn’t have to be inhibited by a lack of understanding. Whether your firm employs a talented IT team to manage and translate your new technology or deploys a no-code solution that’s easy to navigate and requires base-level understanding, there’s a myriad of next-gen solutions available to meet various needs and levels of understanding.

Digital transformation 2.0: The next phase of innovation

Long gone are the days when talking about digital transformation strategies and future plans was enough – there has to be action. Whether you’re ready or not, the competitive landscape is demanding real innovation, meaning it’s time to implement and execute those strategies. The task can seem monumental, especially in such a highly regulated space such as financial services where one wrong move can be costly.

Ironically, the need for this transformation often comes from the regulators’ pace of change, which continues to accelerate, enacting tighter rules and restrictions, sometimes even being put into place due to the functionality of new technologies that offer faster, more accurate and more transparent information.

While the regulatory need is great, new technologies offer far greater benefits than simply achieving regulatory compliance, like having more efficient, streamlined data management that provides actionable insights for better, more intelligent business decisions – a value-add that isn’t possible without the use of AI-embedded technologies.

How to make the most of your data: Your greatest commodity

It’s no secret that data is important, but did you know that between 80-90% of enterprise data is unused, untapped and unavailable, according to McKinsey? And without technology, your data would continue to be unstructured and unanalysed, because of the time and manual effort it would take to manually organise and analyse the information. Think of the competitive advantage of not only having access to that data, but also unlocking the hidden value and insights from that data.

Boiled down, the greatest digital transformation challenge that companies are facing is having an influx of data, with no real roadmap for what to do with it. But by changing the way you manage your data, there’s a critical opportunity to decrease costs, generate efficiencies and save time by reducing manual labour – all while reducing market risk.

As the pandemic spurred market volatility, data transparency and oversight has become top of mind for many financial services firms looking to make the most of their data while also staying compliant with ever-changing regulations.

Leveraging AI tools to automate both processes and analysis allows institutions and financial services firms to increase risk awareness, boost faster reaction times and make more intelligent decisions – all backed up by intuitive, accurate data, that can also offer a deep dive into critical business information, relevant market trends and unique customer behaviours.

With more and more fintechs and ‘techfins’ (like Google and Amazon moving into the financial services space) on the market, established institutions are increasingly in need of a competitive edge. It’s easy to lose focus and think these companies only pose a threat due to their expertise in tech – but that’s not the real threat. It’s the data their technology can access. Without investing in new technologies, innovations and ultimately digital transformation, these companies will continue to encroach into the marketplace, eventually posing an even greater threat.

And unfortunately, research continues to show that of banks – even those who report being around halfway through their digital transformation journey – as little as 14% have actually deployed machine learning tools to date. That means, many of these institutions aren’t prepared for future innovation – and their digital transformation will likely be outdated before they even cross the finish line.

If nothing else, this proves that investing in technology isn’t really just a cost to justify. Instead, it must be an integral part of your long-term growth strategy – one that is not only innovative to meet current standards, but also forward-thinking to meet future needs, expectations and perhaps most importantly, regulations. Because organisations that successfully tap into their valuable sources of data will be armed for success.

With the pressing need to advance digital transformation, the solution is evident: the next phase of digital transformation is now.